Learn Data Science Practically. Analyse Data. Build Case Studies. Prepare for Career Opportunities.
GullyMentor’s Data Science Career Track helps students, freshers, job seekers, and career switchers learn how to explore, clean, analyse, visualise, and present data through mentor-led guidance, live projects, case studies, portfolio support, and career support.
Data Science Unlocks Predictive Patterns in Large Datasets
Beyond tracing what happened, data science uses programming and algorithms to predict trends and automate decision systems. You will learn to clean, query, and model data.
Statistical Modeling
Apply statistics, mathematics, and Python libraries to extract patterns from complex data.
Predictive Power
Build regression, classification, and clustering models to forecast business trends.
Data Engineering
Learn pipeline creation, feature engineering, and database extraction querying.
Project Portfolios
Construct 6 predictive models and documented notebook analyses as hiring proof.
What You Will Learn
Python
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Statistics basics
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Data cleaning
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Data preparation
Normalize dataset values, fill missing parameters, and map qualitative category features.
Exploratory data analysis
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Data visualization
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
ML basics
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Data storytelling
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Case study presentation
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Data interpretation
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Pattern identification
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Business problem framing
Master core concepts, practice hands-on projects, and build verifiable skills in this area.
Who Is This Track Best For?
Students wanting practical Data Science
Ideal for individuals aiming to build career traction, practical tools, and mentor-verified portfolios.
Freshers wanting Python data projects
Ideal for individuals aiming to build career traction, practical tools, and mentor-verified portfolios.
Learners wanting deep analytical skills
Ideal for individuals aiming to build career traction, practical tools, and mentor-verified portfolios.
Exploratory data analysis & case study fans
Ideal for individuals aiming to build career traction, practical tools, and mentor-verified portfolios.
Job seekers wanting portfolio proof
Ideal for individuals aiming to build career traction, practical tools, and mentor-verified portfolios.
Transitioning Data Analytics learners
Data analysts looking to progress from BI dashboards to predictive algorithms.
Build Data Science Project Proof
Build Python files, data scripts, forecasting models, and case reviews in active files.
Customer Behaviour Analysis
Analyse customer data to understand purchase patterns, activity trends, retention behaviour, and business opportunities.
Sales Forecasting Case Study
Study sales data, identify trends, and prepare a forecasting-style case study with observations and recommendations.
EDA Notebook
Create an exploratory data analysis notebook that includes data cleaning, charts, patterns, findings, and conclusions.
Business Data Case Study
Work on a business dataset and prepare a case study that explains the problem, analysis approach, insights, and decisions.
ML-Based Analysis Project
Use basic ML concepts to support a data-driven analysis project and explain the result clearly.
Data Storytelling Report
Prepare a report that converts data analysis into simple insights, visuals, and business-friendly recommendations.
Prepare for Entry-Level Data Science Roles
Important Note: Career direction depends on learner background, project quality, communication, interview readiness, and available opportunities.
Mentorship Packages & Levels
Compare package levels and select the path that aligns with your current learning stage.
| Plans & Features |
Assessment
Career Audit
₹1,000
One-time booking fee
|
Level 1
Foundation
₹15,000
3 Months Mentorship
|
Level 2
Growth
₹30,000
6 Months Mentorship
|
Level 3
Career Launch
₹45,000
6 Months Mentorship
|
|---|---|---|---|---|
| Best For | Learners needing path direction | Beginners seeking foundations | Aspirants seeking strong portfolios | Serious candidates seeking placements |
| Program Focus | A mentor will review your profile, skills, resume, projects, communication, and job-readiness, and provide a personalized recommendation report. | Build your Data Science foundation with Python basics, stats concepts, exploratory analysis introductions, and data visualization tools. | Build EDA Jupyter notebooks, write data cleaning scripts, run ML analysis tests, and format story reports under mentor reviews. | Refine your data science case studies, optimize your portfolio code, prep for analytics questions in interviews, and seek referrals. |
| Duration | 40 Minutes | 3 Months | 6 Months | 6 Months |
| Exploratory Analytics | Reviewed | Basic | Standard structures | Advanced refinement |
| Live Project | Beginner project | Guided project | Advanced/refined project | |
| Resume & LinkedIn | Review only | Basic direction | ||
| Placement Assistance | Based on performance, limited | Based on performance |
Frequently Asked Questions
Ready to Start Your Career Track?
Choose the package that matches your current stage. Start with Career Audit if you need clarity, or join a package directly.